In large-scale industrial e-commerce, the efficiency of an online
recommendation system is crucial in delivering highly relevant item/content
advertising that caters to diverse business scenarios. However, most existing
studies focus solely on item advertising, neglecting the significance of
content advertising. This oversight results in inconsistencies within the
multi-entity structure and unfair retrieval. Furthermore, the challenge of
retrieving top-k advertisements from multi-entity advertisements across
different domains adds to the complexity. Recent research proves that
user-entity behaviors within different domains exhibit characteristics of
differentiation and homogeneity. Therefore, the multi-domain matching models
typically rely on the hybrid-experts framework with domain-invariant and
domain-specific representations. Unfortunately, most approaches primarily focus
on optimizing the combination mode of different experts, failing to address the
inherent difficulty in optimizing the expert modules themselves. The existence
of redundant information across different domains introduces interference and
competition among experts, while the distinct learning objectives of each
domain lead to varying optimization challenges among experts. To tackle these
issues, we propose robust representation learning for the unified online top-k
recommendation. Our approach constructs unified modeling in entity space to
ensure data fairness. The robust representation learning employs domain
adversarial learning and multi-view wasserstein distribution learning to learn
robust representations. Moreover, the proposed method balances conflicting
objectives through the homoscedastic uncertainty weights and orthogonality
constraints. Various experiments validate the effectiveness and rationality of
our proposed method, which has been successfully deployed online to serve real
business scenarios.

在大规模工业电子商务中，强调在线推荐系统的效率对于传递与多样化业务场景相关的高度相关的物品 / 内容广告至关重要。然而，现有研究大多专注于物品广告而忽视了内容广告的重要性，导致多实体结构内的不一致性和不公平检索。本文提出了一种能够解决这些问题的统一在线前 k 推荐的鲁棒表示学习方法，该方法构建了实体空间内的统一建模来确保数据的公平性。通过域对抗学习和多视图 Wasserstein 分布学习来学习鲁棒表示，同时通过同方差权重和正交约束来平衡冲突目标。各种实验证实了我们提出的方法的有效性和合理性，并在实际业务场景中成功部署。

统一在线 Top-K 推荐的稳健表征学习

Robust Representation Learning for Unified Online Top-K Recommendation

Several domains increasingly rely on machine learning in their applications.
The resulting heavy dependence on data has led to the emergence of various laws
and regulations around data ethics and privacy and growing awareness of the
need for privacy-preserving machine learning (ppML). Current ppML techniques
utilize methods that are either purely based on cryptography, such as
homomorphic encryption, or that introduce noise into the input, such as
differential privacy. The main criticism given to those techniques is the fact
that they either are too slow or they trade off a model s performance for
improved confidentiality. To address this performance reduction, we aim to
leverage robust representation learning as a way of encoding our data while
optimizing the privacy-utility trade-off. Our method centers on training
autoencoders in a multi-objective manner and then concatenating the latent and
learned features from the encoding part as the encoded form of our data. Such a
deep learning-powered encoding can then safely be sent to a third party for
intensive training and hyperparameter tuning. With our proposed framework, we
can share our data and use third party tools without being under the threat of
revealing its original form. We empirically validate our results on unimodal
and multimodal settings, the latter following a vertical splitting system and
show improved performance over state-of-the-art.

通过鲁棒表示学习作为编码数据的方式，我们提出了一种旨在优化隐私效用平衡的方法，利用自动编码器进行多目标训练，然后将编码部分的潜在特征和学习特征连接起来作为我们的数据的编码形式，以安全地发送给第三方进行深度训练和超参数调优，从而在维护数据隐私的同时提高性能。

隐私保护机器学习的鲁棒表示学习：一种多目标自编码器方法

Robust Representation Learning for Privacy-Preserving Machine Learning:  A Multi-Objective Autoencoder Approach

During the past decade, deep neural networks have led to fast-paced progress
and significant achievements in computer vision problems, for both academia and
industry. Yet despite their success, state-of-the-art image classification
approaches fail to generalize well in previously unseen visual contexts, as
required by many real-world applications. In this paper, we focus on this
domain generalization (DG) problem and argue that the generalization ability of
deep convolutional neural networks can be improved by taking advantage of
multi-layer and multi-scaled representations of the network. We introduce a
framework that aims at improving domain generalization of image classifiers by
combining both low-level and high-level features at multiple scales, enabling
the network to implicitly disentangle representations in its latent space and
learn domain-invariant attributes of the depicted objects. Additionally, to
further facilitate robust representation learning, we propose a novel objective
function, inspired by contrastive learning, which aims at constraining the
extracted representations to remain invariant under distribution shifts. We
demonstrate the effectiveness of our method by evaluating on the domain
generalization datasets of PACS, VLCS, Office-Home and NICO. Through extensive
experimentation, we show that our model is able to surpass the performance of
previous DG methods and consistently produce competitive and state-of-the-art
results in all datasets.

通过结合多层和多尺度的特征，利用深度卷积神经网络来提高图像分类器的领域泛化能力，并提出了一种新颖的目标函数，使用对比学习的方法约束提取的特征在分布转移下保持不变，实验证明该方法在多个数据集上均表现出与之前方法相比更好的性能。